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Table of Contents

• What's New
• Commercial Text-to-Video Products
  • Generative Text-to-Video Models
  • AI Avatar / Presenter Tools
  • All-in-One Creative Platforms
  • Discontinued / Historical
  • Featured Product Cards
  • Related Tools
  • How to Choose
• Open-Source Models & Toolkits
  • Quick Start
• Research Papers
  • 2026
  • 2025
  • 2024
  • 2023
  • 2022
  • Before 2021
• Datasets & Benchmarks
• Contributing
• License
• References

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What's New

2026 Update: The T2V landscape has shifted dramatically. OpenAI discontinued the consumer Sora app in early 2026, while open-source models (Wan 2.2, HunyuanVideo 1.5, LTX-2.3) and commercial alternatives (Runway Gen-4.5, Seedance 2.0, Kling 3.0, Veo 3.1) now dominate.

Data collected as of June 2026. Prices, features, and availability change quickly — always check the official website before making a decision.

Key trends:

• Native 4K and synchronized audio are becoming standard.
• Open-source models now rival closed commercial systems.
• Multi-shot storytelling and character consistency are the new battlegrounds.
• Inference acceleration (sliding tile attention, token carving, caching) makes local generation feasible.

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Commercial Text-to-Video Products

Closed-Source Platforms

Generative Text-to-Video Models

AI Avatar / Presenter Tools

All-in-One Creative Platforms

Discontinued / Historical

Featured Product Cards

Related Tools

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How to Choose

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Open-Source Models & Toolkits

Quick Start

Get started with the most popular open-source models:

Wan 2.2

git clone https://github.com/Wan-Video/Wan2.2
cd Wan2.2
pip install -r requirements.txt

HunyuanVideo 1.5

git clone https://github.com/Tencent-Hunyuan/HunyuanVideo
cd HunyuanVideo
# Follow the official guide for sampling and I2V inference

CogVideoX

git clone https://github.com/THUDM/CogVideo
cd CogVideo
pip install -r requirements.txt

For detailed inference configs, LoRA fine-tuning, and ComfyUI workflows, see each repository's README.

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Research Papers

2026

• Efficient Video Diffusion Models: Advancements and Challenges, Shitong Shao. [Paper]
  • First comprehensive survey focused on efficient video diffusion: step distillation, efficient attention, model compression, caching/trajectory optimization.
• Partition the Support, Reconstruct the Residual: Training-Free Sparse Attention for Video Generation and World Models, Pardis Taghavi, Reza Langari, and Gaurav Pandey. [Paper] [Project]
  • Training-free sparse attention using response-coupled partitioning and probe-fitted residual reconstruction, evaluated across four video generation and world models.
• Controllable Video Generation: A Survey [Paper]
  • Systematic review of pose-guided, structure-controlled, and other conditional video generation methods.
• Survey of Video Diffusion Models: Foundations, Implementations, and Applications, Yimu Wang et al. [Paper]
  • Comprehensive taxonomy of diffusion-based video generation, evaluation metrics, industry solutions, and training engineering.
• UNIC: Unified In-Context Video Editing [Paper]
• BRITE: A Benchmark for Reliable and Interpretable T2V Evaluation on Implausible Scenarios [Paper]
• Runway Gen-4.5 Technical Report — Runway (2026) [Runway Help]
• ByteDance Seedance 2.0 — Global launch April 2026 [Overview]
• LTX-2.3: Native 4K Video + Audio Generation — Lightricks (March 2026) [Project]

2025

• Bridging Text and Video Generation: A Survey, Nilay Kumar et al. [Paper]
  • Comprehensive survey on T2V evolution, DiT architectures, datasets, training recipes, and benchmarks.
• Wan 2.2: Mixture-of-Experts Video Generation — Alibaba (July 2025) [Project]
• Wan: Open and Advanced Large-Scale Video Generative Models, Alibaba. [Paper] [Code]
• Training a Commercial-Level Video Generation Model in $200k (Open-Sora 2.0) [Paper] [Code]
• Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model, Guoqing Ma et al. [Paper] [Code]
• HunyuanVideo: A Systematic Framework For Large Video Generative Models, Weijie Kong et al. [Paper] [Code]
• Fast Video Generation with Sliding Tile Attention, Peiyuan Zhang et al. [Paper]
• Training-Free Efficient Video Generation via Dynamic Token Carving [Paper]
• FastCar: Cache Attentive Replay for Fast Auto-Regressive Video Generation on the Edge [Paper]
• On-Device Sora: Enabling Training-Free Diffusion-Based Text-to-Video Generation for Mobile Devices [Paper]
• SkyReels-A2: Compose Anything in Video Diffusion Transformers [Paper]
• MotionCanvas: Cinematic Shot Design with Controllable Image-to-Video Generation [Paper]
• EasyControl: Adding Efficient and Flexible Control for Diffusion Transformer [Paper]
• MUG-V 10B: High-efficiency Training Pipeline for Large Video Generation Models [Paper]
• VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness [Paper] [Code]
• Kandinsky 5.0: A Family of Foundation Models for Image and Video Generation [Paper]
• ARLON: Boosting Diffusion Transformers with Autoregressive Models for Long Video Generation [Paper]
• T2VTextBench: A Human Evaluation Benchmark for Textual Control in Video Generation Models [Paper]
• A Survey of AI-Generated Video Evaluation [Paper]

2024

• Movie Gen: A Cast of Media Foundation Models, Meta. [Paper]
• Lumiere: A Space-Time Diffusion Model for Video Generation, Google DeepMind. [Paper] [Project] [Code]
• Vidu: A Highly Consistent, Dynamic and Skilled Text-to-Video Generator with Diffusion Models, Shengshu / Tsinghua. [Paper] [Code]
• Allegro: Open the Black Box of Commercial-Level Video Generation Model, Rhymes AI. [Paper] [Code]
• DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors, CUHK / Tencent AI Lab. [Paper] [Code]
• Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models, Yixin Liu et al. [Paper]
• VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models, Haoxin Chen et al. [Paper] [Code]
• CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer, Zhuoyi Yang et al. [Paper] [Code]
• Latte: Latent Diffusion Transformer for Video Generation, Xin Ma et al. [Paper] [Code]
• VideoTetris: Towards Compositional Text-to-Video Generation [Paper]
• MagicTime: Time-lapse Video Generation Models as Metamorphic Simulators [Paper]
• T2V-CompBench: A Comprehensive Benchmark for Compositional Text-to-Video Generation [Paper]
• CPA: Camera-pose-awareness Diffusion Transformer for Video Generation [Paper]
• Open-Sora: Democratizing Efficient Video Production for All [Project]
• Open-Sora-Plan: Open-Source Reproduction of Sora [Project]

2023

• Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets, Andreas Blattmann et al. [Paper]
• ModelScope Text-to-Video Technical Report, Jiayu Wang et al. [Paper]
• I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models, Alibaba / DAMO. [Paper] [Code]
• AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning, Yuwei Guo et al. [Paper] [Code]
• Scalable Diffusion Models with Transformers (DiT), William Peebles & Saining Xie. [Paper] — Foundation for diffusion-transformer architectures used in Sora and successors.
• Video Generation Models as World Simulators, OpenAI (Sora technical report). [Report]
• Text-To-4D Dynamic Scene Generation, Uriel Singer et al. [Paper] [Project]

2022

• Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation, Jay Zhangjie Wu et al. [Paper] [Project] [Code]
• MagicVideo: Efficient Video Generation With Latent Diffusion Models, Daquan Zhou et al. [Paper] [Project]
• Phenaki: Variable Length Video Generation From Open Domain Textual Description, Ruben Villegas et al. [Paper]
• Imagen Video: High Definition Video Generation with Diffusion Models, Jonathan Ho et al. [Paper] [Project]
• Text-driven Video Prediction, Xue Song et al. [Paper]
• Make-A-Video: Text-to-Video Generation without Text-Video Data, Uriel Singer et al. [Paper] [Project] [Code]
• StoryDALL-E: Adapting Pretrained Text-to-Image Transformers for Story Continuation, Adyasha Maharana et al. [Paper] [Code]
• Word-Level Fine-Grained Story Visualization, Bowen Li et al. [Paper] [Code]
• CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers, Wenyi Hong et al. [Paper] [Code]
• Show Me What and Tell Me How: Video Synthesis via Multimodal Conditioning, Yogesh Balaji et al. [Paper] [Code] [Project]
• Video Diffusion Models, Jonathan Ho et al. [Paper] [Project]

Before 2021

• Transcript to Video: Efficient Clip Sequencing from Texts, Ligong Han et al. [Paper] [Project]
• GODIVA: Generating Open-DomaIn Videos from nAtural Descriptions, Chenfei Wu et al. [Paper]
• Text2Video: Text-driven Talking-head Video Synthesis with Phonetic Dictionary, Sibo Zhang et al. [Paper]
• TiVGAN: Text to Image to Video Generation With Step-by-Step Evolutionary Generator, Doyeon Kim et al. [Paper]
• Conditional GAN with Discriminative Filter Generation for Text-to-Video Synthesis, Yogesh Balaji et al. [Paper] [Code]
• IRC-GAN: Introspective Recurrent Convolutional GAN for Text-to-video Generation, Kangle Deng et al. [Paper]
• StoryGAN: A Sequential Conditional GAN for Story Visualization, Yitong Li et al. [Paper] [Code]
• Video Generation From Text, Yitong Li et al. [Paper]
• To create what you tell: Generating videos from captions, Yingwei Pan et al. [Paper]

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Datasets & Benchmarks

Datasets

Benchmarks

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Contributing

Contributions are welcome! Please open a pull request to add new products, papers, models, datasets, or benchmarks. Keep entries concise and include both a paper link and a code/project link when available.

If you have any questions, feel free to contact jianzhnie.

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License

Awesome-Text-To-Video is released under the Apache 2.0 license.

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References

This list was compiled and updated using information from the following sources.

Product Websites

• OpenAI Sora
• Google DeepMind Veo
• Runway • Runway Help: Creating with Gen-4 Video • Runway Review 2026
• Luma Dream Machine
• Kling AI
• Hailuo AI
• Pika
• Krea AI
• ByteDance Seedance • Seedance 2.0 Global Release Guide • Seedance 2.0 Review
• PixVerse
• Morphic / Morph Studio
• Hedra
• Vidnoz AI
• Steve.AI
• Vidu
• Lumiere Project Page
• Synthesia
• HeyGen
• DeepBrain AI
• Colossyan
• Elai.io
• D-ID
• Hour One
• Creatify
• InVideo AI
• Magic Hour
• videos.social

Open-Source Repositories

• Wan 2.1
• HunyuanVideo
• CogVideo
• Open-Sora
• Open-Sora-Plan
• LTX-Video
• Mochi
• Step-Video-T2V
• SkyReels-V2
• MAGI-1
• Waver
• VideoCrafter
• VGen / ModelScope T2V
• Allegro
• DynamiCrafter
• I2VGen-XL
• VideoComposer
• Lumiere PyTorch
• AnimateDiff
• Latte

Datasets, Benchmarks & Surveys

• VBench
• T2V-CompBench
• T2VTextBench
• ChronoMagic-Bench
• VideoEval
• arXiv papers cited in the Research Papers section.